Bayesian Active Meta-Learning for Black-Box Optimization
This work addresses data-efficient learning for applications like wireless system deployment where labeling is expensive, but it appears incremental as it builds on existing meta-learning and active learning concepts.
The paper tackles the problem of reducing labeling costs in meta-learning by introducing an information-theoretic active task selection mechanism, and it demonstrates this approach for Bayesian optimization of black-box models, though no concrete numbers are provided in the abstract.
Data-efficient learning algorithms are essential in many practical applications for which data collection is expensive, e.g., for the optimal deployment of wireless systems in unknown propagation scenarios. Meta-learning can address this problem by leveraging data from a set of related learning tasks, e.g., from similar deployment settings. In practice, one may have available only unlabeled data sets from the related tasks, requiring a costly labeling procedure to be carried out before use in meta-learning. For instance, one may know the possible positions of base stations in a given area, but not the performance indicators achievable with each deployment. To decrease the number of labeling steps required for meta-learning, this paper introduces an information-theoretic active task selection mechanism, and evaluates an instantiation of the approach for Bayesian optimization of black-box models.